{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Quantile Regression"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import statsmodels.api as sm\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yahoo finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-11-20T04:58:43.429Z",
          "iopub.execute_input": "2022-11-20T04:58:43.433Z",
          "shell.execute_reply": "2022-11-20T04:58:44.477Z",
          "iopub.status.idle": "2022-11-20T04:58:44.483Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "                           Open  High   Low  Close  Adj Close    Volume\nDate                                                                   \n2014-01-02 00:00:00-05:00  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03 00:00:00-05:00  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06 00:00:00-05:00  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07 00:00:00-05:00  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08 00:00:00-05:00  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02 00:00:00-05:00</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03 00:00:00-05:00</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06 00:00:00-05:00</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07 00:00:00-05:00</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08 00:00:00-05:00</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-11-20T04:58:44.488Z",
          "iopub.execute_input": "2022-11-20T04:58:44.491Z",
          "shell.execute_reply": "2022-11-20T04:58:45.549Z",
          "iopub.status.idle": "2022-11-20T04:58:45.510Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.tail()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "                                Open   High        Low      Close  Adj Close  \\\nDate                                                                           \n2018-08-20 00:00:00-04:00  19.790001  20.08  19.350000  19.980000  19.980000   \n2018-08-21 00:00:00-04:00  19.980000  20.42  19.860001  20.400000  20.400000   \n2018-08-22 00:00:00-04:00  20.280001  20.92  20.209999  20.900000  20.900000   \n2018-08-23 00:00:00-04:00  21.190001  22.32  21.139999  22.290001  22.290001   \n2018-08-24 00:00:00-04:00  22.910000  24.00  22.670000  23.980000  23.980000   \n\n                              Volume  \nDate                                  \n2018-08-20 00:00:00-04:00   62983200  \n2018-08-21 00:00:00-04:00   55629000  \n2018-08-22 00:00:00-04:00   62002700  \n2018-08-23 00:00:00-04:00  113444100  \n2018-08-24 00:00:00-04:00  164328200  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2018-08-20 00:00:00-04:00</th>\n      <td>19.790001</td>\n      <td>20.08</td>\n      <td>19.350000</td>\n      <td>19.980000</td>\n      <td>19.980000</td>\n      <td>62983200</td>\n    </tr>\n    <tr>\n      <th>2018-08-21 00:00:00-04:00</th>\n      <td>19.980000</td>\n      <td>20.42</td>\n      <td>19.860001</td>\n      <td>20.400000</td>\n      <td>20.400000</td>\n      <td>55629000</td>\n    </tr>\n    <tr>\n      <th>2018-08-22 00:00:00-04:00</th>\n      <td>20.280001</td>\n      <td>20.92</td>\n      <td>20.209999</td>\n      <td>20.900000</td>\n      <td>20.900000</td>\n      <td>62002700</td>\n    </tr>\n    <tr>\n      <th>2018-08-23 00:00:00-04:00</th>\n      <td>21.190001</td>\n      <td>22.32</td>\n      <td>21.139999</td>\n      <td>22.290001</td>\n      <td>22.290001</td>\n      <td>113444100</td>\n    </tr>\n    <tr>\n      <th>2018-08-24 00:00:00-04:00</th>\n      <td>22.910000</td>\n      <td>24.00</td>\n      <td>22.670000</td>\n      <td>23.980000</td>\n      <td>23.980000</td>\n      <td>164328200</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-11-20T04:58:45.515Z",
          "iopub.execute_input": "2022-11-20T04:58:45.517Z",
          "iopub.status.idle": "2022-11-20T04:58:45.525Z",
          "shell.execute_reply": "2022-11-20T04:58:45.551Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Y = dataset['Adj Close']\n",
        "X = dataset['Low']"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-20T04:58:45.530Z",
          "iopub.execute_input": "2022-11-20T04:58:45.533Z",
          "iopub.status.idle": "2022-11-20T04:58:45.537Z",
          "shell.execute_reply": "2022-11-20T04:58:45.553Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#fit the model\n",
        "model = smf.quantreg('Y ~ X', dataset).fit(q=0.9)"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-11-20T04:58:45.542Z",
          "iopub.execute_input": "2022-11-20T04:58:45.545Z",
          "shell.execute_reply": "2022-11-20T04:58:45.592Z",
          "iopub.status.idle": "2022-11-20T04:58:45.568Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#view model summary\n",
        "print(model.summary())"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                         QuantReg Regression Results                          \n",
            "==============================================================================\n",
            "Dep. Variable:                      Y   Pseudo R-squared:               0.9660\n",
            "Model:                       QuantReg   Bandwidth:                     0.05644\n",
            "Method:                 Least Squares   Sparsity:                       0.7401\n",
            "Date:                Sat, 19 Nov 2022   No. Observations:                 1171\n",
            "Time:                        20:58:45   Df Residuals:                     1169\n",
            "                                        Df Model:                            1\n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept      0.0132      0.012      1.098      0.272      -0.010       0.037\n",
            "X              1.0433      0.001    709.548      0.000       1.040       1.046\n",
            "==============================================================================\n"
          ]
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-11-20T04:58:45.573Z",
          "iopub.execute_input": "2022-11-20T04:58:45.576Z",
          "iopub.status.idle": "2022-11-20T04:58:45.583Z",
          "shell.execute_reply": "2022-11-20T04:58:45.595Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#define figure and axis\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "\n",
        "#get y values\n",
        "get_y = lambda a, b: a + b * X\n",
        "y = get_y(model.params['Intercept'], model.params['X'])\n",
        "\n",
        "#plot data points with quantile regression equation overlaid\n",
        "ax.plot(X, Y, color='black')\n",
        "ax.scatter(X, Y, alpha=.3)\n",
        "ax.set_xlabel('Low', fontsize=14)\n",
        "ax.set_ylabel('Close', fontsize=14)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "Text(0, 0.5, 'Close')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 576x432 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 7,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false,
        "execution": {
          "iopub.status.busy": "2022-11-20T04:58:45.601Z",
          "iopub.execute_input": "2022-11-20T04:58:45.604Z",
          "iopub.status.idle": "2022-11-20T04:58:45.691Z",
          "shell.execute_reply": "2022-11-20T04:58:45.695Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}